• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.514-521
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• 2016

The High Efficiency Video Coding (HEVC) standard provides superior coding efficiency by utilizing highly flexible block structure and more diverse coding modes. However, rate-distortion optimization (RDO) process for the decision of optimal block size and prediction mode requires excessive computational complexity. To alleviate the computation load, this paper proposes a new moment-based fast CU size decision algorithm for intra coding in HEVC. In the proposed method, moment values are computed in each CU block to estimate the texture complexity of the block from which the decision on an additional CU splitting procedure is performed. Unlike conventional methods which are mostly variance-based approaches, the proposed method incorporates the third-order moments of the CU block in the design of the fast CU size decision algorithm, which enables an elaborate classification of CU types and thus improves the RD-performance of the fast algorithm. Experimental results show that the proposed method saves 32% encoding time with 1.1% increase of BD-rate compared to HM-10.0, and 4.2% decrease of BD-rate compared to the conventional variance-based fast algorithm.

• ETRI Journal
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• v.35 no.2
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• pp.270-280
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• 2013

A fast intra-prediction method is proposed for High Efficiency Video Coding (HEVC) using a fast intra-mode decision and fast coding unit (CU) size decision. HEVC supports very sophisticated intra modes and a recursive quadtree-based CU structure. To provide a high coding efficiency, the mode and CU size are selected in a rate-distortion optimized manner. This causes a high computational complexity in the encoder, and, for practical applications, the complexity should be significantly reduced. In this paper, among the many predefined modes, the intra-prediction mode is chosen without rate-distortion optimization processes, instead using the difference between the minimum and second minimum of the rate-distortion cost estimation based on the Hadamard transform. The experiment results show that the proposed method achieves a 49.04% reduction in the intra-prediction time and a 32.74% reduction in the total encoding time with a nearly similar coding performance to that of HEVC test model 2.1.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.959-974
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• 2020

High efficiency video coding (HEVC) employs quadtree coding tree unit (CTU) structure to improve its coding efficiency, but at the same time, it also requires a very high computational complexity due to its exhaustive search processes for an optimal coding unit (CU) partition. With the aim of solving the problem, a fast CU size decision optimal algorithm based on neighborhood prediction is presented for HEVC in this paper. The contribution of this paper lies in the fact that we successfully use the partition information of neighborhood CUs in different depth to quickly determine the optimal partition mode for the current CU by neighborhood prediction technology, which can save much computational complexity for HEVC with negligible RD-rate (rate-distortion rate) performance loss. Specifically, in our scheme, we use the partition information of left, up, and left-up CUs to quickly predict the optimal partition mode for the current CU by neighborhood prediction technology, as a result, our proposed algorithm can effectively solve the problem above by reducing many unnecessary prediction and partition operations for HEVC. The simulation results show that our proposed fast CU size decision algorithm based on neighborhood prediction in this paper can reduce about 19.0% coding time, and only increase 0.102% BD-rate (Bjontegaard delta rate) compared with the standard reference software of HM16.1, thus improving the coding performance of HEVC.

• Journal of Broadcast Engineering
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• v.19 no.3
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• pp.405-414
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• 2014

High Efficiency Video Coding(HEVC) obtains high compression ratio by applying recursive quad-tree structured coding unit(CU). However, this recursive quad-tree structure brings very high computational complexity to HEVC encoder. In this paper, we present fast CU decision algorithm in recursive quad-tree structure. The proposed algorithm estimates initial CU size before CTU encoding and checks the proposed condition using Coded Block Flag(CBF) and Rate-distortion cost to achieve the fast encoding time saving. And, intra mode estimation is also possible to be skipped using the CBF values acquired during the inter PU mode estimations. Experiment results shows that the proposed algorithm saved about 49.91% and 37.97% of encoding time according to the weighting condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1730-1747
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• 2018

Multi-view video plus depth (MVD) is a mainstream format of 3D scene representation in free viewpoint video systems. The advanced 3D extension of the high efficiency video coding (3D-HEVC) standard introduces new prediction tools to improve the coding performance of depth video. However, the depth video in 3D-HEVC is time consuming. To reduce the complexity of the depth video inter coding, we propose a fast coding unit (CU) size and mode decision algorithm. First, an off-line trained Bayesian model is built which the feature vector contains the depth levels of the corresponding spatial, temporal, and inter-component (texture-depth) neighboring largest CUs (LCUs). Then, the model is used to predict the depth level of the current LCU, and terminate the CU recursive splitting process. Finally, the CU mode search process is early terminated by making use of the mode correlation of spatial, inter-component (texture-depth), and inter-view neighboring CUs. Compared to the 3D-HEVC reference software HTM-10.0, the proposed algorithm reduces the encoding time of depth video and the total encoding time by 65.03% and 41.04% on average, respectively, with negligible quality degradation of the synthesized virtual view.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1195-1211
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• 2016

The emerging video coding standard High Efficiency Video Coding (HEVC) has shown almost 40% bit-rate reduction over the state-of-the-art Advanced Video Coding (AVC) standard but at about 40% computational complexity overhead. The main reason for HEVC computational complexity is the inter prediction that accounts for 60%-70% of the whole encoding time. In this paper, we propose several fast coding unit (CU) encoding schemes based on the Merge mode and motion estimation information to reduce the computational complexity caused by the HEVC inter prediction. Firstly, an early Merge mode decision method based on motion estimation (EMD) is proposed for each CU size. Then, a Merge mode based early termination method (MET) is developed to determine the CU size at an early stage. To provide a better balance between computational complexity and coding efficiency, several fast CU encoding schemes are surveyed according to the rate-distortion-complexity characteristics of EMD and MET methods as a function of CU sizes. These fast CU encoding schemes can be seamlessly incorporated in the existing control structures of the HEVC encoder without limiting its potential parallelization and hardware acceleration. Experimental results demonstrate that the proposed schemes achieve 19%-46% computational complexity reduction over the HEVC test model reference software, HM 16.4, at a cost of 0.2%-2.4% bit-rate increases under the random access coding configuration. The respective values under the low-delay B coding configuration are 17%-43% and 0.1%-1.2%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3286-3300
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• 2016

High Efficiency Video Coding (HEVC) Standard, as the latest coding standard, introduces satisfying compression structures with respect to its predecessor Advanced Video Coding (H.264/AVC). The new coding standard can offer improved encoding performance compared with H.264/AVC. However, it also leads to enormous computational complexity that makes it considerably difficult to be implemented in real time application. In this paper, based on machine learning, a fast partitioning method is proposed, which can search for the best splitting structures for Intra-Prediction. In view of the video texture characteristics, we choose the entropy of Gray-Scale Difference Statistics (GDS) and the minimum of Sum of Absolute Transformed Difference (SATD) as two important features, which can make a balance between the computation complexity and classification performance. According to the selected features, adaptive decision trees can be built for the Coding Units (CU) with different size by offline training. Furthermore, by this way, the partition of CUs can be resolved as a binary classification problem. Experimental results have shown that the proposed algorithm can save over 34% encoding time on average, with a negligible Bjontegaard Delta (BD)-rate increase.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.2271-2288
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• 2015

The emerging high efficiency video coding (HEVC) standard adopts the quadtree-structured transform unit (TU) in the residual quadtree (RQT) coding. Each TU allows to be split into four equal sub-TUs recursively. The RQT coding is performed for all the possible transform depth levels to achieve the highest coding efficiency, but it requires a very high computational complexity for HEVC encoders. In order to reduce the computational complexity requested by the RQT coding, in this paper, we propose a fast TU size decision method incorporating an adaptive maximum transform depth determination (AMTD) algorithm and a full check skipping - early termination (FCS-ET) algorithm. Because the optimal transform depth level is highly content-dependent, it is not necessary to perform the RQT coding at all transform depth levels. By the AMTD algorithm, the maximum transform depth level is determined for current treeblock to skip those transform depth levels rarely used by its spatially adjacent treeblocks. Additionally, the FCS-ET algorithm is introduced to exploit the correlations of transform depth level between four sub-CUs generated by one coding unit (CU) quadtree partitioning. Experimental results demonstrate that the proposed overall algorithm significantly reduces on average 21% computational complexity while maintaining almost the same rate distortion (RD) performance as the HEVC test model reference software, HM 13.0.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.147-158
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• 2021

Versatile Video Coding (VVC) is the latest video coding standard developed by Joint Video Exploration Team (JVET). In VVC, the quadtree plus multi-type tree (QT+MTT) structure of coding unit (CU) partition is adopted, and its computational complexity is considerably high due to the brute-force search for recursive rate-distortion (RD) optimization. In this paper, we aim to reduce the time complexity of inter-picture prediction mode since the inter prediction accounts for a large portion of the total encoding time. The problem can be defined as classifying the split mode of each CU. To classify the split mode effectively, a novel convolutional neural network (CNN) called multi-level tree (MLT-CNN) architecture is introduced. For boosting classification performance, we utilize additional information including inter-picture information while training the CNN. The overall algorithm including the MLT-CNN inference process is implemented on VVC Test Model (VTM) 11.0. The CUs of size 128×128 can be the inputs of the CNN. The sequences are encoded at the random access (RA) configuration with five QP values {22, 27, 32, 37, 42}. The experimental results show that the proposed algorithm can reduce the computational complexity by 11.53% on average, and 26.14% for the maximum with an average 1.01% of the increase in Bjøntegaard delta bit rate (BDBR). Especially, the proposed method shows higher performance on the sequences of the A and B classes, reducing 9.81%~26.14% of encoding time with 0.95%~3.28% of the BDBR increase.